Pyroptosis-Related Gene Signature Predicts Prognosis and Indicates Immune Microenvironment Infiltration in Glioma

Caspase-4 in glioma indicates deterioration and unfavorable prognosis by affecting tumor cell proliferation and immune cell recruitment

Gliomas are the most common malignant tumors of the central nervous system, accounting for approximately 80% of all malignant brain tumors. Accumulating evidence suggest that pyroptosis plays an essential role in the progression of cancer. Unfortunately, the effect of the pyroptosis-related factor caspase-4 (CASP4) on immunotherapy and drug therapy for tumors has not been comprehensively investigated. In this study, we systematically screened six hub genes by pooling differential pyroptosis-related genes in The Cancer Genome Atlas (TCGA) glioma data and the degree of centrality of index-related genes in the protein-protein interaction network. We performed functional and pathway enrichment analyses of the six hub genes to explore their biological functions and potential molecular mechanisms. We then investigated the importance of CASP4 using Kaplan-Meier survival analysis of glioma patients. TCGA and the Chinese Glioma Genome Atlas (CGGA) databases showed that reduced CASP4 expression leads to the potent clinical deterioration of glioma patients. Computational analysis of the effect of CASP4 on the infiltration level and recruitment of glioma immune cells revealed that CASP4 expression was closely associated with a series of tumor-suppressive immune checkpoint molecules, chemokines, and chemokine receptors. We also found that aberrant CASP4 expression correlated with chemotherapeutic drug sensitivity. Finally, analysis at the cellular and tissue levels indicated an increase in CASP4 expression in glioma, and that CASP4 inhibition significantly inhibited the proliferation of glioma cells. Thus, CASP4 is implicated as a new prognostic biomarker for gliomas with the potential to further guide immunotherapy and chemotherapy strategies for glioma patients.

Challenges of Regulated Cell Death: Implications for Therapy Resistance in Cancer

Regulated cell death, a regulatory form of cell demise, has been extensively studied in multicellular organisms. It plays a pivotal role in maintaining organismal homeostasis under normal and pathological conditions. Although alterations in various regulated cell death modes are hallmark features of tumorigenesis, they can have divergent effects on cancer cells. Consequently, there is a growing interest in targeting these mechanisms using small-molecule compounds for therapeutic purposes, with substantial progress observed across various human cancers. This review focuses on summarizing key signaling pathways associated with apoptotic and autophagy-dependent cell death. Additionally, it explores crucial pathways related to other regulated cell death modes in the context of cancer. The discussion delves into the current understanding of these processes and their implications in cancer treatment, aiming to illuminate novel strategies to combat therapy resistance and enhance overall cancer therapy.

Programmed cell death disrupts inflammatory tumor microenvironment (TME) and promotes glioblastoma evolution

Glioblastoma (GBM) is the most common malignant brain tumor and has a dismal prognosis even under the current first-line treatment, with a 5-year survival rate less than 7%. Therefore, it is important to understand the mechanism of treatment resistance and develop new anti-tumor strategies. Induction of programmed cell death (PCD) has become a promising anti-tumor strategy, but its effectiveness in treating GBM remains controversial. On the one hand, PCD triggers tumor cell death and then release mediators to draw in immune cells, creating a pro-inflammatory tumor microenvironment (TME). One the other hand, mounting evidence suggests that PCD and inflammatory TME will force tumor cells to evolve under survival stress, leading to tumor recurrence. The purpose of this review is to summarize the role of PCD and inflammatory TME in the tumor evolution of GBM and promising methods to overcome tumor evolution.

Xihuang pill facilitates glioma cell pyroptosis via the POU4F1/STAT3 axis

Glioma is the most common malignancy in the central nervous system. This study aims to disclose the impacts of Xihuang pill (XHP), a traditional Chinese formula, on glioma cell pyroptosis and relevant molecular mechanism. U251 and SHG-44 cells were treated with XHP alone or together with oe-POU4F1 and sh-STAT3. CCK8 assay detected the viability, flow cytometry evaluated pyroptosis, and microscopy observed cell morphology. LDH release was determined by the LDH kit and the levels of IL-1β and IL-18 were detected by ELISA. Immunofluorescence showed NLRP3 expression in glioma cells and western blotting measured the levels of POU4F1, STAT3, NLRP3, ASC, cleaved caspase-1, and IL-1β. The binding of POU4F1 to STAT3 was verified. Primary glioma model was established to observe tumor change by in vivo imaging, determine the levels of Ki67 and NLRP3 by immunochemistry, and detect relevant protein levels by western blotting. XHP treatment alone downregulated POU4F1 and STAT3 levels, aroused pyroptotic appearance in glioma cells such as ballooning swelling, reduced cell viability and number of pyroptotic cells, increased LDH release and IL-1β and IL-18 levels, formed NLRP3 sports in cells, and elevated the levels of pyroptosis-related proteins. However, POU4F1 overexpression or STAT3 silencing suppressed XHP-promoted pyroptosis. Mechanistically, POU4F1 acted as a transcription factor of STAT3 and regulated its transcription. In primary glioma models, XHP enhanced glioma cell pyroptosis and blocked glioma growth. XHP facilitates glioma cell pyroptosis via the POU4F1/STAT3 axis.

A potential novel biomarker: comprehensive analysis of prognostic value and immune implication of CES3 in colonic adenocarcinoma

PURPOSE

Colon cancer is the most common malignant tumor in the intestine. Abnormal Carboxylesterases 3 (CES3) expression had been reported to be correlated to multiple tumor progression. However, the association among CES3 expression and prognostic value and immune effects in colonic adenocarcinoma (COAD) were unclear.

PATIENTS AND METHODS

The transcription and expression data of CES3 and corresponding clinical information was downloaded from The Cancer Genome Atlas (TCGA). The CES3 protein expression and the prognostic value were verified based on tissue microarray data. The Cancer immune group Atlas (TCIA), Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and the GSE78220 immunotherapy cohort were used to forecast immunotherapy efficacy. Finally, a prognostic immune signature was constructed and verified.

RESULTS

Compared with normal colon tissues, the expression of mRNA and protein levels of CES3 were downregulated in tumor tissues. CES3 expression was associated with TIICs. Hihg-CES3 COAD patients had better efficacy of concurrent immunotherapy. CES3-related immune genes (CRIs) were identified and were then used to construct prognostic immune signature and had been successfully verified in GES39582.

CONCLUSION

CES3 might be a potential immune-related gene and promising prognostic biomarker in COAD.

The evaluation of six genes combined value in glioma diagnosis and prognosis

PURPOSE

Glioma is the most common and fatal type of brain tumour. Owing to its aggressiveness and lethality, early diagnosis and prediction of patient survival are very important. This study aimed to identify key genes and biomarkers for glioma that can guide clinicians in making rapid diagnosis and prognostication.

METHODS

Data mining of The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), Repository of Molecular Brain Neoplasia Data, and Genotype-Tissue Expression Project brain expression data revealed significantly differentially expressed genes (DEGs), and the risk scores of individual patients were calculated. WGCNA was utilized to screen for genes most related to clinical diagnosis. Prognostic genes associated with glioma were selected via combining the LASSO regression with univariate and multivariate Cox regression and protein-protein interaction network analyses. Then, a nomogram was constructed. And CGGA dataset was utilized to validated. The protein expression levels of the signature were detected using the human protein atlas. Drug response prediction was carried out using the package "pRRophetic".

RESULTS

A six-gene signature (KLF6, CHI3L1, SERPINE1, ANGPT2, TGFBR1, and PTX3) was identified and used to stratify patients into low- and high-risk groups. Survival, ROC curve, and Cox analyses clarified that the six hub genes were a favourable independent prognostic factor for patients with glioma. A nomogram was set up by integrating clinical parameters with risk signatures, showing high precision for predicting 2-, 3-, 4-, 5-years survival. In addition, the expression of most genes was consistent with protein expression. Furthermore, the sensitivity to the top ten drugs in the GDSC database of the high-risk group was significantly higher than the low-risk group.

CONCLUSION

Based on genetic profiles and clinicopathological features, including age, grade, isocitrate dehydrogenase mutation status, we constructed a comprehensive prognostic model for patients with glioma. These signatures can be regarded as biomarkers to predict the prognosis of gliomas, possibly providing more therapeutic strategies for future clinical research.

A pyroptosis-related signature predicts prognosis and indicates immune microenvironment infiltration in glioma

BACKGROUND

Glioma, the most common malignant brain tumor, leads to high recurrence rates and disabilities in patients. Pyroptosis is an inflammasomes-induced programmed cell death in response to infection or chemotherapy. However, the role of pyroptosis in glioma has not yet been elucidated.

METHODS

RNA-seq data and clinical information of 660 gliomas and 847 samples were downloaded from the TCGA and CGGA, respectively. Then, data of 104 normal brain tissues was retrieved from the GTEx for differential expression analysis. Twelve pairs of peritumoral tissue and glioma samples were used for validation. Gene alteration status of differentially expressed pyroptosis-related regulators in gliomas was detected in cBioPortal algorithm. Consensus clustering was employed to classify gliomas based on differentially expressed pyroptosis-related regulators. Subsequently, a PS-signature was constructed using LASSO-congressional analysis for clinical application. The immune infiltration of glioma microenvironment (TME) was explored using ESTIMATE, CIBERSORT, and the other immune signatures.

RESULTS

cBioPortal algorithm revealed alteration of these regulators was correlated to better prognosis of gliomas. Then, our study showed that pyroptosis-related regulators can be used to sort out patients into two clusters with distinct prognostic outcome and immune status. Moreover, a PS-signature for predicting the prognosis of glioma patients was developed based on the identified subtypes. The high PS-score group showed more abundant inflammatory cell infiltration and stronger immune response, but with poorer prognosis of gliomas.

CONCLUSION

The findings of this study provide a therapeutic basis for future research on pyroptosis and unravel the relationship between pyroptosis and glioma prognosis. The risk signature can be utilized as a prognostic biomarker for glioma.

A Novel Prognostic Pyroptosis-Related Gene Signature Correlates to Oxidative Stress and Immune-Related Features in Gliomas

Gliomas are highly invasive and aggressive tumors having the highest incidence rate of brain cancer. Identifying effective prognostic and potential therapeutic targets is necessitated. The relationship of pyroptosis, a form of programmed cellular death, with gliomas remains elusive. We constructed and validated a prognostic model for gliomas using pyroptosis-related genes. Differentially expressed pyroptosis-related genes were screened using the "limma" package. Based on LASSO-Cox regression, nine significant genes including CASP1, CASP3, CASP6, IL32, MKI67, MYD88, PRTN3, NOS1, and VIM were employed to construct a prognostic model in the TCGA cohort; the results were validated in the CGGA cohort. According to the median risk score, the patients were classified into two risk groups, namely, high- and low-risk groups. Patients at high risk had worse prognoses relative to those at low risk evidenced by the Kaplan-Meier curve analysis. The two groups exhibited differences in immune cell infiltration and TMB scores, with high immune checkpoint levels, TMB scores, and immune cell infiltration levels in the high-risk group. KEGG and GO analyses suggested enrichment in immune-related pathways. Furthermore, we found that the genes in our signature strongly correlated with oxidative stress-related pathways and the subgroups exhibited different ssGSEA scores. Some small molecules targeted the genes in the model, and we verified their drug sensitivities between the risk groups. The scRNA-seq dataset, GSE138794, was processed using the "Seurat" package to assess the level of risk gene expression in specific cell types. Finally, the MYD88 level was lowered in the U87 glioma cell line using si-RNA constructs. Cellular proliferation was impaired, and fewer pyroptosis-related cytokines were released upon exposure to LPS. In summary, we built a pyroptosis-related gene model that accurately classified glioma patients into high- and low-risk groups. The findings suggest that the signature may be an effective prognostic predictive tool for gliomas.

SARS-CoV-2 Pattern Provides a New Scoring System and Predicts the Prognosis and Immune Therapeutic Response in Glioma

OBJECTIVE

Glioma is the most common primary malignancy of the adult central nervous system (CNS), with a poor prognosis and no effective prognostic signature. Since late 2019, the world has been affected by the rapid spread of SARS-CoV-2 infection. Research on SARS-CoV-2 is flourishing; however, its potential mechanistic association with glioma has rarely been reported. The aim of this study was to investigate the potential correlation of SARS-CoV-2-related genes with the occurrence, progression, prognosis, and immunotherapy of gliomas.

METHODS

SARS-CoV-2-related genes were obtained from the human protein atlas (HPA), while transcriptional data and clinicopathological data were obtained from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Glioma samples were collected from surgeries with the knowledge of patients. Differentially expressed genes were then identified and screened, and seven SARS-CoV-2 related genes were generated by LASSO regression analysis and uni/multi-variate COX analysis. A prognostic SARS-CoV-2-related gene signature (SCRGS) was then constructed based on these seven genes and validated in the TCGA validation cohort and CGGA cohort. Next, a nomogram was established by combining critical clinicopathological data. The correlation between SCRGS and glioma related biological processes was clarified by Gene set enrichment analysis (GSEA). In addition, immune infiltration and immune score, as well as immune checkpoint expression and immune escape, were further analyzed to assess the role of SCRGS in glioma-associated immune landscape and the responsiveness of immunotherapy. Finally, the reliability of SCRGS was verified by quantitative real-time polymerase chain reaction (qRT-PCR) on glioma samples.

RESULTS

The prognostic SCRGS contained seven genes, REEP6, CEP112, LARP4B, CWC27, GOLGA2, ATP6AP1, and ERO1B. Patients were divided into high- and low-risk groups according to the median SARS-CoV-2 Index. Overall survival was significantly worse in the high-risk group than in the low-risk group. COX analysis and receiver operating characteristic (ROC) curves demonstrated excellent predictive power for SCRGS for glioma prognosis. In addition, GSEA, immune infiltration, and immune scores indicated that SCRGS could potentially predict the tumor microenvironment, immune infiltration, and immune response in glioma patients.

CONCLUSIONS

The SCRGS established here can effectively predict the prognosis of glioma patients and provide a potential direction for immunotherapy.

A prognostic pyroptosis-related LncRNA classifier associated with the immune landscape and therapy efficacy in glioma

Background: Glioma has the highest fatality rate among intracranial tumours. Besides, the heterogeneity of gliomas leads to different therapeutic effects even with the same treatment. Developing a new signature for glioma to achieve the concept of “personalised medicine” remains a significant challenge.

Method: The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) were searched to acquire information on glioma patients. Initially, correlation and univariate Cox regression analyses were performed to screen for prognostic pyroptosis-related long noncoding RNAs (PRLs). Secondly, 11 PRLs were selected to construct the classifier using certain algorithms. The efficacy of the classifier was then detected by the “timeROC” package for both the training and validation datasets. CIBERSORT and ESTIMATE packages were applied for comparing the differences (variations) in the immune landscape between the high- and low-risk groups. Finally, the therapeutic efficacy of the chemotherapy, radiotherapy, and immunotherapy were assessed using the “oncoPredict” package, survival analysis, and the tumour immune dysfunction and exclusion (TIDE) score, respectively.

Results: A classifier comprising 11 PRLs was constructed. The PRL classifier exhibits a more robust prediction capacity for the survival outcomes in patients with gliomas than the clinical characteristics irrespective of the dataset (training or validation dataset). Moreover, it was found that the tumour landscape between the low- and high-risk groups was significantly different. A high-risk score was linked to a more immunosuppressive tumour microenvironment. According to the outcome prediction and analysis of the chemotherapy, patients with different scores showed different responses to various chemotherapeutic drugs and immunotherapy. Meanwhile, the patient with glioma of WHO grade Ⅳ or aged >50 years in the high risk group had better survival following radiotherapy.

Conclusion: We constructed a PRL classifier to roughly predict the outcome of patients with gliomas. Furthermore, the PRL classifier was linked to the immune landscape of glioma and may guide clinical treatments.

Construction and validation of an angiogenesis-related gene expression signature associated with clinical outcome and tumor immune microenvironment in glioma

Background: Glioma is the most prevalent malignant intracranial tumor. Many studies have shown that angiogenesis plays a crucial role in glioma tumorigenesis, metastasis, and prognosis. In this study, we conducted a comprehensive analysis of angiogenesis-related genes (ARGs) in glioma.

Methods: RNA-sequencing data of glioma patients were obtained from TCGA and CGGA databases. Via consensus clustering analysis, ARGs in the sequencing data were distinctly classified into two subgroups. We performed univariate Cox regression analysis to determine prognostic differentially expressed ARGs and least absolute shrinkage and selection operator Cox regression to construct a 14-ARG risk signature. The CIBERSORT algorithm was used to explore immune cell infiltration, and the ESTIMATE algorithm was applied to calculate immune and stromal scores.

Results: We found that the 14-ARG signature reflected the infiltration characteristics of different immune cells in the tumor immune microenvironment. Additionally, total tumor mutational burden increased significantly in the high-risk group. We combined the 14-ARG signature with patient clinicopathological data to construct a nomogram for predicting 1-, 3-, and 5-year overall survival with good accuracy. The predictive value of the prognostic model was verified in the CGGA cohort. SPP1 was a potential biomarker of glioma risk and was involved in the proliferation, invasion, and angiogenesis of glioma cells.

Conclusion: In conclusion, we established and validated a novel ARG risk signature that independently predicted the clinical outcomes of glioma patients and was associated with the tumor immune microenvironment.